Picked Coding Bugs
In this article, we will explore and understand the concept of different solutions approaches and take a beatable approach to clarify our data and along with that, we still need something that, actually works for us and brings nostalgic results, we discussing the concept of features engineering, if you are a machine learning bug, then you heard this sounds interesting, actually whenever we build a ML model, different features handling are most important based on our data nature, so we can use it in that too, and learn what it exactly do for us and finally we folks landing out to the discussion of trend features and classic STA/LTA approaches.
Table of Content
- Solution approach
- Feature engineering
- Trend features and classic STA/LTA
Solution Approach
The task in the competition is to accurately forecast a singular
time_to_failure value for each segment in the test dataset. Each segment of
the test set comprises 150,000 data rows. In contrast, the training dataset
is vast, encompassing 692 million rows, with one column dedicated to our
target variable, the time until failure. We plan to divide the training data into uniform segments, each containing 150,000 rows, and use the final time-to-
failure value from each segment as the target variable for that segment. This
approach is designed to align the training data with the format of the test
data, facilitating more effective model training. Additionally, we will engineer new features by aggregating values across
both the training and test datasets, resulting in a single row that encapsulates
multiple features for each data segment. The subsequent section will delve
into the signal processing techniques employed for feature generation.
Feature Engineering
We will use several libraries specific to signal processing to generate most of
the features. From SciPy, Python scientific library, we are using a few
functions from the signal module. The Hann function returns a Hann
window, which modifies the signal to smooth the values at the end of the
sampled signal to 0 uses a cosine “bell” function. The Hilbert function
computes the analytic signal using the Hilbert transform. The Hilbert
transform is a mathematical technique used in signal processing, with a
property that shifts the phase of the original signal by 90 degrees.
Other library functions used are from numpy, Fast Fourier Transform (FFT),
mean, min, max, std (standard deviation), abs (absolute value), diff (the
difference between two successive values in the signal and quantile
where a sample is divided into equal-sized, adjacent groups. We are also
using a few statistical functions that are available from pandas, mad median
absolute deviation, kurtosis, skew, and median. We are implementing
functions to calculate trend features and classic STA/LTA. Classic STA/LTA
represents the ratio between the amplitude of the signal of a short time
window of length STA and a long time window, LTA.
Trend Features and Classic STA/LTA
We start by defining two functions, for the calculation of a trend feature and
classic Short Term Average/Long-Term Average (STA/LTA). STA/LTA is
a seismic signal analysis technique used in seismology. It measures the ratio
of short term to long term signal averages. It is useful in earthquake
detection as it identifies distinct patterns in seismic data. Therefore, it will
also be a useful feature to include in our model.
We show here the code to calculate the trend feature. This is calculated using
a linear regression model for 1D data and retrieves the slope of the
resulting regression line. We use the option to transform all the sampled data
into positive values before performing regression that is, calculating the
slope/trend for the absolute values of the data. The trend data contains
important information about the overall signal.
Next, we calculate the classic STA/LTA, which represents the ratio between
the amplitude of the signal of a short time window of length STA and a long
time window, LTA. The function receives as parameters the signal and the
length for the short-time average and long time average windows.
Next, we implement the function to calculate features, which receives as
parameters the sample index, the data subsample and a handle to the
transformed training data. This function will use various signal processing algorithms to build aggregated features from the time variation acoustic
signal per segment. In the case of the training data, we use windows of 150K
rows from the training set without stride. In the case of the test set, each
the test file represents a segment of 150K. In the following subsections, we will
review the engineered features that will be included in the model.
Conclusion
Finally, we are catching the concepts of solution approach, feature engineering and classic STA/LTA approaches. We will explore how the machine learning model acts according to our commands and handlings, when we build a model, it doesn’t satisfy us by its accuracy and precision, so that we take these types of different techniques that still help us for bringing out best and great results, obviously if you are familiar with coding, that it is a football game for you, kick out different solutions and tactics and run the program by taking a glass of juice.
